Plot annotations after reclustering

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Last updated: 2025-10-27

Checks: 5 2

Knit directory: ~/SPP1_mouse_scRNAseq/

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---

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/home/hnatri/SPP1_mouse_scRNAseq/	.
/home/hnatri/SPP1_mouse_scRNAseq/code/CART_plot_functions.R	code/CART_plot_functions.R
/home/hnatri/SPP1_mouse_scRNAseq/code/colors_themes.R	code/colors_themes.R
/home/hnatri/SPP1_mouse_scRNAseq/updated_cluster_markers_top50.tsv	updated_cluster_markers_top50.tsv

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/update_annotations.Rmd) and HTML (docs/update_annotations.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	32c5b8d	heinin	2025-10-26	Updated index and marker expression plots
html	32c5b8d	heinin	2025-10-26	Updated index and marker expression plots
Rmd	4fcd1c0	heinin	2025-09-26	Updating annotations after refiltering
html	4fcd1c0	heinin	2025-09-26	Updating annotations after refiltering

---

Libraries and environment variables

library(workflowr)
library(Seurat)
library(googlesheets4)
library(tidyverse)
library(plyr)
library(ggrepel)
library(patchwork)

setwd("/home/hnatri/SPP1_mouse_scRNAseq/")
set.seed(1234)
options(future.globals.maxSize = 30000 * 1024^2)
reduction <- "integratedSCTumap"

source("/home/hnatri/SPP1_mouse_scRNAseq/code/CART_plot_functions.R")
source("/home/hnatri/SPP1_mouse_scRNAseq/code/colors_themes.R")
source("/home/hnatri/SingleCellBestPractices/scripts/preprocessing_qc_module.R")
source("/home/hnatri/SingleCellBestPractices/scripts/integration_module.R")

# Cluster annotations
gs4_deauth()
cluster_annot  <- gs4_get("https://docs.google.com/spreadsheets/d/127J6C4KF7uBGKUnrPuC1mcsb_wNCN6k1zXKSCbJ6q0M/edit?usp=sharing")
cluster_annot <- read_sheet(cluster_annot, sheet = "Cluster annotation")

Helper functions

recluster_seurat <- function(seurat_object, vars_to_regress){
  
  DefaultAssay(seurat_object) <- "RNA"
  seurat_object <- SCTransform(seurat_object, vars.to.regress = vars_to_regress)
  seurat_object <- RunPCA(seurat_object,
                          reduction.name = "pca",
                          verbose = F)
  pcs <- get_pcs(seurat_object, reduction_name = "pca")
  message(pcs)
  seurat_object <- RunUMAP(seurat_object,
                           reduction = "pca",
                           reduction.name = "umap",
                           dims = 1:min(pcs),
                           return.model = TRUE)
  seurat_object <- FindNeighbors(seurat_object,
                                 reduction = "pca",
                                 dims = 1:min(pcs),
                                 graph.name = c("nn",
                                                "snn"))
  seurat_object <- FindClusters(seurat_object,
                                resolution = c(0.1,0.2,0.3,0.5,0.8,1),
                                graph.name = "snn")
  
  return(seurat_object)
}

Import data

seurat_data <- readRDS("/tgen_labs/banovich/BCTCSF/SPP1_mouse_scRNAseq/scRNAseq_Seurat_dim8_nCount1k_nFeature500_dblrate15_scGSEAmodules.rds")

old_seurat_data <- readRDS("/tgen_labs/banovich/BCTCSF/SPP1_mouse_scRNAseq/scRNAseq_Seurat_dim8_granular_annot.rds")

length(setdiff(colnames(old_seurat_data), colnames(seurat_data)))
length(setdiff(colnames(seurat_data), colnames(old_seurat_data)))
length(intersect(colnames(old_seurat_data), colnames(seurat_data)))

unique(old_seurat_data$DF.class_col)
unique(old_seurat_data$doublet_finder)

seurat_data@meta.data[rownames(seurat_data@meta.data) %in% setdiff(colnames(seurat_data), colnames(old_seurat_data)),]

old_seurat_annot <- old_seurat_data@meta.data[,c("orig.ident", "annot_granular")]
rm(old_seurat_data)
rm(seurat_data)

Reconstructing the UMAP and reclustering

vars_to_regress <- c("G2M.Score",
                     "S.Score",
                     "nCount_RNA",
                     "nFeature_RNA",
                     "percent.mt")

seurat_data <- recluster_seurat(seurat_data, vars_to_regress = vars_to_regress)

#saveRDS(seurat_data, "/tgen_labs/banovich/BCTCSF/SPP1_mouse_scRNAseq/scRNAseq_Seurat_dim8_nCount1k_nFeature500_dblrate15.rds_scGSEAmicroglia_reclustered.rds")

Add old annotations

seurat_data <- readRDS("/tgen_labs/banovich/BCTCSF/SPP1_mouse_scRNAseq/scRNAseq_Seurat_dim8_nCount1k_nFeature500_dblrate15_scGSEAmodules.rds")

seurat_data$annot_granular <- mapvalues(colnames(seurat_data),
                                        from = rownames(old_seurat_annot),
                                        to = old_seurat_annot$annot_granular)

seurat_data$annot_granular <- ifelse(colnames(seurat_data) %in% rownames(old_seurat_annot), seurat_data$annot_granular, NA)

#saveRDS(seurat_data, "/tgen_labs/banovich/BCTCSF/SPP1_mouse_scRNAseq/scRNAseq_Seurat_dim8_nCount1k_nFeature500_dblrate15_scGSEAmodules_reclustered_refAnnot.rds")

Plotting

seurat_data <- readRDS("/tgen_labs/banovich/BCTCSF/SPP1_mouse_scRNAseq/scRNAseq_Seurat_dim8_nCount1k_nFeature500_dblrate15_scGSEAmodules_reclustered_refAnnot.rds")

DimPlot(seurat_data,
        group.by = "snn_res.0.8",
        reduction = "umap",
        label = T) +
  coord_fixed() +
  theme_classic() +
  NoLegend()

Past versions of unnamed-chunk-6-1.png

Version	Author	Date
4fcd1c0	heinin	2025-09-26

DimPlot(seurat_data,
        group.by = "annot_granular",
        reduction = "umap",
        label = T) +
  coord_fixed() +
  theme_classic()

Past versions of unnamed-chunk-7-1.png

Version	Author	Date
4fcd1c0	heinin	2025-09-26

DimPlot(seurat_data,
        group.by = "predicted.id",
        reduction = "umap",
        label = T) +
  coord_fixed() +
  theme_classic()

Past versions of unnamed-chunk-7-2.png

Version	Author	Date
4fcd1c0	heinin	2025-09-26

table(seurat_data$snn_res.0.8, seurat_data$annot_granular)
table(seurat_data$snn_res.0.8, seurat_data$predicted.id)
table(seurat_data$annot_granular, seurat_data$predicted.id)

DimPlot(seurat_data,
        group.by = "Phase",
        reduction = "umap",
        label = T) +
  coord_fixed() +
  theme_classic()

Past versions of unnamed-chunk-8-1.png

Version	Author	Date
4fcd1c0	heinin	2025-09-26

# Annotation colors
annots_granular <- sort(unique(seurat_data$annot_granular))
annots_granular_col <- colorRampPalette(brewer.pal(10, "Paired"))(nb.cols <- length(annots_granular))
names(annots_granular_col) <- annots_granular

create_barplot(seurat_data,
               group_var = "snn_res.0.8",
               plot_var = "annot_granular",
               plot_levels = sort(unique(seurat_data$annot_granular)),
               group_levels = sort(unique(seurat_data$snn_res.0.8)),
               plot_colors = annots_granular_col,
               var_names =  c("Frequency", ""),
               legend_title = "")

Past versions of unnamed-chunk-9-1.png

Version	Author	Date
4fcd1c0	heinin	2025-09-26

# Cluster colors
snn_res.0.8_clusters <- as.factor(c(0, seq(1:33)))
snn_res.0.8_cluster_col <- colorRampPalette(brewer.pal(10, "Paired"))(nb.cols <- length(snn_res.0.8_clusters))
names(snn_res.0.8_cluster_col) <- levels(snn_res.0.8_clusters)

create_barplot(seurat_data,
               group_var = "annot_granular",
               plot_var = "snn_res.0.8",
               plot_levels = levels(seurat_data$snn_res.0.8),
               group_levels = sort(unique(seurat_data$annot_granular)),
               plot_colors = snn_res.0.8_cluster_col,
               var_names =  c("Frequency", ""),
               legend_title = "")

Past versions of unnamed-chunk-10-1.png

Version	Author	Date
4fcd1c0	heinin	2025-09-26

# Reference-based annotation colors
ref_annot_clusters <- sort(unique(seurat_data$predicted.id))
ref_annot_cluster_col <- colorRampPalette(brewer.pal(10, "Paired"))(nb.cols <- length(ref_annot_clusters))
names(ref_annot_cluster_col) <- levels(ref_annot_clusters)

create_barplot(seurat_data,
               group_var = "annot_granular",
               plot_var = "predicted.id",
               plot_levels = levels(seurat_data$predicted.id),
               group_levels = sort(unique(seurat_data$annot_granular)),
               plot_colors = ref_annot_cluster_col,
               var_names =  c("Frequency", ""),
               legend_title = "")

Past versions of unnamed-chunk-11-1.png

Version	Author	Date
4fcd1c0	heinin	2025-09-26

create_barplot(seurat_data,
               group_var = "predicted.id",
               plot_var = "annot_granular",
               plot_levels = levels(seurat_data$annot_granular),
               group_levels = sort(unique(seurat_data$predicted.id)),
               plot_colors = annots_granular_col,
               var_names =  c("Frequency", ""),
               legend_title = "")

Past versions of unnamed-chunk-12-1.png

Version	Author	Date
4fcd1c0	heinin	2025-09-26

create_barplot(seurat_data,
               group_var = "snn_res.0.8",
               plot_var = "predicted.id",
               plot_levels = levels(seurat_data$predicted.id),
               group_levels = sort(unique(seurat_data$snn_res.0.8)),
               plot_colors = ref_annot_cluster_col,
               var_names =  c("Frequency", ""),
               legend_title = "")

create_barplot(seurat_data,
               group_var = "predicted.id",
               plot_var = "snn_res.0.8",
               plot_levels = levels(seurat_data$snn_res.0.8),
               group_levels = sort(unique(seurat_data$predicted.id)),
               plot_colors = snn_res.0.8_cluster_col,
               var_names =  c("Frequency", ""),
               legend_title = "")

qc_features <- c("nCount_RNA", "nFeature_RNA", "percent.mt")

FeaturePlot(seurat_data,
            #layer = "RNA",
            #slot = "data",
            features = qc_features,
            order = T,
            ncol = 3,
            reduction = "umap",
            raster = T,
            cols = c("gray89", "tomato3")) &
  coord_fixed(ratio = 1) &
  theme_classic()

Human markers

plot_features <- c("PTMA", "PFN1", "CFL1", "TMSB4X", "TPT1", "TMSB10", "MIF",
                   "PDPN", "NLRP3", "IL1B", "CCL4", "S100A8", "S100A9",
                   "S100A10", "TYROBP", "CD68", "ICAM1", "C1QA", "C1QB", "C1QC",
                   "CD74", "AREG", "CD4", "APOE", "FABP5", "SPP1", "CD274",
                   "CD96", "PTPRC", "CEMIP2", "KLRD1", "CD8A", "NKG7", "IL32",
                   "CD3D", "BTG1", "IFITM2", "ITM2A", "SELL", "GZMB", "CD79A",
                   "ACTA2", "PDGFRB", "COL1A1", "CD163", "MRC1", "ITGAM", "CD14",
                   "CD279", "PDCD1", "TREM2", "TMEM119", "P2RY12", "CX3CR1")

DefaultAssay(seurat_data) <- "RNA_human"
FeaturePlot(seurat_data,
            #assay = "RNA_human",
            slot = "data",
            features = plot_features,
            order = T,
            ncol = 5,
            reduction = "umap",
            raster = T,
            cols = c("gray89", "tomato3")) &
  coord_fixed(ratio = 1) &
  theme_bw() &
  NoLegend()

Mouse expression modules

FeaturePlot(seurat_data,
            features = c("DAM", "Homeostatic", "MHC_I_machinery", "MHC_II_machinery", "Costimulation_APCB", "Adhesion_immunesynapse"),
            order = T,
            ncol = 3,
            reduction = "umap",
            raster = T,
            cols = c("gray89", "tomato3")) &
  coord_fixed(ratio = 1) &
  theme_bw() &
  NoLegend()

Expression of each mouse module gene

# Gene sets
gs4_deauth()
genesets  <- gs4_get("https://docs.google.com/spreadsheets/d/127J6C4KF7uBGKUnrPuC1mcsb_wNCN6k1zXKSCbJ6q0M/edit?usp=sharing")
genesets <- read_sheet(genesets, sheet = "Myeloid modules")

DefaultAssay(seurat_data) <- "RNA"
FeaturePlot(seurat_data,
            features = unique(genesets$gene),
            order = T,
            ncol = 5,
            reduction = "umap",
            raster = T,
            cols = c("gray89", "tomato3")) &
  coord_fixed(ratio = 1) &
  theme_bw() &
  NoLegend()

Top markers for new clusters

markers <- presto::wilcoxauc(seurat_data,
                             group_by = "snn_res.0.8",
                             assay = "data",
                             seurat_assay = "RNA_human")

top_features <- markers %>%  group_by(group) %>% slice_max(order_by = auc, n = 5)

DefaultAssay(seurat_data) <- "RNA_human"
create_dotplot_heatmap(seurat_object = seurat_data,
                       plot_features = unique(top_features$feature),
                       group_var = "snn_res.0.8",
                       group_colors = snn_res.0.8_cluster_col,
                       column_title = "",
                       row_km = 5,
                       col_km = 5,
                       row.order = NULL,
                       col.order = NULL)

output_cluster_markers <- markers %>%
  arrange(dplyr::desc(logFC)) %>%
  group_by(group) %>%
  dplyr::slice(1:50)

#write.table(output_cluster_markers, "/home/hnatri/SPP1_mouse_scRNAseq/updated_cluster_markers_top50.tsv",
#            quote = F, row.names = F, sep = "\t")

Removing problem clusters

Updating annotations

# To build on command line, run Rscript -e "rmarkdown::render('update_annotations.Rmd')"
# Then "mv *.html /home/hnatri/SPP1_mouse_scRNAseq/docs/"

Session information

sessionInfo()

R version 4.3.0 (2023-04-21)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: Etc/UTC
tzcode source: system (glibc)

attached base packages:
[1] stats4    grid      stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] mclust_6.0.0                scCustomize_1.1.1          
 [3] glmGamPoi_1.14.3            DoubletFinder_2.0.4        
 [5] scater_1.30.1               scuttle_1.12.0             
 [7] SingleCellExperiment_1.24.0 SummarizedExperiment_1.32.0
 [9] Biobase_2.62.0              GenomicRanges_1.54.1       
[11] GenomeInfoDb_1.38.5         IRanges_2.36.0             
[13] S4Vectors_0.40.2            BiocGenerics_0.48.1        
[15] MatrixGenerics_1.14.0       matrixStats_1.0.0          
[17] SoupX_1.6.2                 circlize_0.4.15            
[19] ComplexHeatmap_2.18.0       viridis_0.6.3              
[21] viridisLite_0.4.2           RColorBrewer_1.1-3         
[23] patchwork_1.1.2             ggrepel_0.9.3              
[25] plyr_1.8.8                  lubridate_1.9.2            
[27] forcats_1.0.0               stringr_1.5.0              
[29] dplyr_1.1.2                 purrr_1.0.1                
[31] readr_2.1.4                 tidyr_1.3.0                
[33] tibble_3.2.1                ggplot2_3.4.2              
[35] tidyverse_2.0.0             googlesheets4_1.1.0        
[37] Seurat_5.0.1                SeuratObject_5.0.1         
[39] sp_1.6-1                    workflowr_1.7.1            

loaded via a namespace (and not attached):
  [1] fs_1.6.2                  spatstat.sparse_3.0-1    
  [3] bitops_1.0-7              httr_1.4.6               
  [5] doParallel_1.0.17         tools_4.3.0              
  [7] sctransform_0.4.1         utf8_1.2.3               
  [9] R6_2.5.1                  lazyeval_0.2.2           
 [11] uwot_0.1.14               GetoptLong_1.0.5         
 [13] withr_2.5.0               gridExtra_2.3            
 [15] progressr_0.13.0          cli_3.6.1                
 [17] Cairo_1.6-0               spatstat.explore_3.2-1   
 [19] fastDummies_1.7.3         labeling_0.4.2           
 [21] sass_0.4.6                spatstat.data_3.0-1      
 [23] ggridges_0.5.4            pbapply_1.7-0            
 [25] parallelly_1.36.0         rstudioapi_0.14          
 [27] generics_0.1.3            shape_1.4.6              
 [29] ica_1.0-3                 spatstat.random_3.1-5    
 [31] Matrix_1.6-5              ggbeeswarm_0.7.2         
 [33] fansi_1.0.4               abind_1.4-5              
 [35] lifecycle_1.0.3           whisker_0.4.1            
 [37] yaml_2.3.7                snakecase_0.11.0         
 [39] SparseArray_1.2.3         Rtsne_0.16               
 [41] paletteer_1.5.0           promises_1.2.0.1         
 [43] crayon_1.5.2              miniUI_0.1.1.1           
 [45] lattice_0.21-8            beachmat_2.18.1          
 [47] cowplot_1.1.1             magick_2.7.4             
 [49] pillar_1.9.0              knitr_1.43               
 [51] rjson_0.2.21              future.apply_1.11.0      
 [53] codetools_0.2-19          leiden_0.4.3             
 [55] glue_1.6.2                getPass_0.2-4            
 [57] data.table_1.14.8         vctrs_0.6.2              
 [59] png_0.1-8                 spam_2.9-1               
 [61] cellranger_1.1.0          gtable_0.3.3             
 [63] rematch2_2.1.2            cachem_1.0.8             
 [65] xfun_0.39                 S4Arrays_1.2.0           
 [67] mime_0.12                 survival_3.5-5           
 [69] gargle_1.4.0              iterators_1.0.14         
 [71] ellipsis_0.3.2            fitdistrplus_1.1-11      
 [73] ROCR_1.0-11               nlme_3.1-162             
 [75] RcppAnnoy_0.0.20          rprojroot_2.0.3          
 [77] bslib_0.4.2               irlba_2.3.5.1            
 [79] vipor_0.4.5               KernSmooth_2.23-21       
 [81] colorspace_2.1-0          ggrastr_1.0.2            
 [83] tidyselect_1.2.0          processx_3.8.1           
 [85] compiler_4.3.0            curl_5.0.0               
 [87] git2r_0.32.0              BiocNeighbors_1.20.2     
 [89] DelayedArray_0.28.0       plotly_4.10.2            
 [91] scales_1.2.1              lmtest_0.9-40            
 [93] callr_3.7.3               digest_0.6.31            
 [95] goftest_1.2-3             presto_1.0.0             
 [97] spatstat.utils_3.0-3      rmarkdown_2.22           
 [99] XVector_0.42.0            htmltools_0.5.5          
[101] pkgconfig_2.0.3           sparseMatrixStats_1.14.0 
[103] highr_0.10                fastmap_1.1.1            
[105] rlang_1.1.1               GlobalOptions_0.1.2      
[107] htmlwidgets_1.6.2         shiny_1.7.4              
[109] DelayedMatrixStats_1.24.0 farver_2.1.1             
[111] jquerylib_0.1.4           zoo_1.8-12               
[113] jsonlite_1.8.5            BiocParallel_1.36.0      
[115] BiocSingular_1.18.0       RCurl_1.98-1.12          
[117] magrittr_2.0.3            GenomeInfoDbData_1.2.11  
[119] dotCall64_1.0-2           munsell_0.5.0            
[121] Rcpp_1.0.10               reticulate_1.29          
[123] stringi_1.7.12            zlibbioc_1.48.0          
[125] MASS_7.3-60               parallel_4.3.0           
[127] listenv_0.9.0             deldir_1.0-9             
[129] splines_4.3.0             tensor_1.5               
[131] hms_1.1.3                 ps_1.7.5                 
[133] igraph_1.4.3              spatstat.geom_3.2-1      
[135] RcppHNSW_0.5.0            reshape2_1.4.4           
[137] ScaledMatrix_1.10.0       evaluate_0.21            
[139] ggprism_1.0.4             tzdb_0.4.0               
[141] foreach_1.5.2             httpuv_1.6.11            
[143] RANN_2.6.1                polyclip_1.10-4          
[145] future_1.32.0             clue_0.3-64              
[147] scattermore_1.2           rsvd_1.0.5               
[149] janitor_2.2.0             xtable_1.8-4             
[151] RSpectra_0.16-1           later_1.3.1              
[153] googledrive_2.1.0         beeswarm_0.4.0           
[155] cluster_2.1.4             timechange_0.2.0         
[157] globals_0.16.2